Water Pollution Prediction Based on Deep Belief Network in Big Data of Water Environment Monitoring

Li Liang,Ahmed Farouk

doi:10.1155/2021/8271950

Abstract

Aiming at the problems that the traditional water quality prediction model is generally not high in prediction accuracy and robustness, a water pollution prediction using deep learning in water environment monitoring big data is proposed. Objective. To optimize and improve the prediction accuracy of the water quality prediction model. Firstly, in the water environment monitoring system, the Internet of Things big data technology is used to accurately sense and monitor the real-time data of sewage treatment equipment and sewage quality. Then, the deep belief network (DBN) is used to build the water pollution prediction model, and the collected sewage treatment data is analyzed to predict the water quality status. Finally, particle swarm optimization algorithm is used to dynamically optimize the number of hidden layer neural units and learning rate in the DBN prediction model, which makes the prediction results more scientific and accurate. Based on the sampling data of Shanghai Jinze Reservoir, the proposed model is experimentally analyzed. The results show that the probability of accurate location of the pollution source is not less than 70%. And under the two indicators of chemical oxygen demand and biological oxygen demand, the root mean square error and correlation coefficient are 3.073, 0.9892 and 1.958, 0.9565, respectively, which are better than other comparison models.

Highlights

In recent years, with the rapid development of cities and social economy, the issue of water resources has gradually become a hot social issue
Faced with the massive detection data generated by many sensors in the water supply network, its analysis and judgment require updated technical support. rough the study of water resources prediction models, the use of water environment monitoring big data to predict the pollution of water sources is the key research direction [6, 7]
In the water quality pollution prediction system architecture based on deep learning in the water environment monitoring big data, the overall topological structure and functional structure of the system are mainly designed, and the overall design and implementation of the system are planned [22]. e system design goals mainly include two aspects: water quality data collection based on big data of the Internet of ings, water quality pollution prediction and control based on deep learning

Summary

Introduction

With the rapid development of cities and social economy, the issue of water resources has gradually become a hot social issue. Reference [12] proposed a prediction model based on nonlinear regression for the problem of irrigation water quality. It has flexible and accurate evaluation performance for irrigation water quality. Reference [17] proposed a seawater quality prediction method based on artificial neural network and multiple linear regression model. Reference [18] combines convolutional neural network and long-shortterm memory model to predict water quality, which has good accuracy and predictive performance. Aiming at the problem that traditional prediction models cannot handle massive data from multiple sensors, a water pollution prediction model using deep learning in the big data of water environment monitoring is proposed. In order to improve its convergence speed and generalization ability, the prediction results are more scientific and accurate

Related Technology

System Structure

Data Preprocessing

Conclusion